Richard W Sias

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Journals/Publications

• Sias, R. W., McLemore, P., Wan, C., & Yuksel, Z. (2020). Active Technological Similarity and Mutual Fund Performance. Journal of Financial and Quantitative Analysis.
• Sias, R. W., Sias, R. W., Kalcheva, I., Kalcheva, I., Sias, R. W., McLemore, P., Mclemore, P., & Kalcheva, I. (2021). Economic Policy Uncertainty and Self-Control: Evidence from Unhealthy Choices. Journal of Financial and Quantitative Analysis, 56(4), 1446-1475.
• Sias, R. W., DeVault, L., & Starks, L. (2017). Sentiment Metrics and Investor Demand. Journal of Finance.
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  Recent work suggests that sentiment traders shift from safer to more speculative stocks when sentiment increases. Exploiting these cross-sectional patterns and changes in share ownership, we find that sentiment metrics capture institutional rather than individual investors’ demand shocks. We investigate the underlying economic mechanisms and find that common institutional investment styles (e.g., risk management, momentum trading) explain a significant portion of the relation between institutions and sentiment.
• Starks, L. T., Sias, R. W., & Devault, L. A. (2019). Sentiment Metrics and Investor Demand. Journal of Finance, 74(2), 985-1024. doi:10.1111/jofi.12754
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  Recent work suggests that sentiment traders shift from safer to more speculative stocks when sentiment increases. Exploiting these cross‐sectional patterns and changes in share ownership, we find that sentiment metrics capture institutional rather than individual investors’ demand shocks. We investigate the underlying economic mechanisms and find that common institutional investment styles (e.g., risk management, momentum trading) explain a significant portion of the relation between institutions and sentiment.
• Sias, R. W., Turtle, H., & Zykaj, B. (2018). Reconsidering Hedge Fund Contagion. Journal of Alternative Investments, 21(1), 27-38. doi:https://doi.org/10.3905/jai.2018.21.1.027
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  A widely held view holds that hedge funds act as a negative disruptive force in financial markets due to “contagion.” For example, hedge funds are often viewed as culprits in both the 2007-2008 financial crisis and the 2007 quant crisis. The authors evaluate existing and new evidence of: (1) hedge fund contagion, (2) hedge fund crowding, (3) hedge funds’ role in the 2007-2008 financial crisis, and (4) hedge funds’ role in the August 2007 quant crisis. Contrary to conventional wisdom, the popular press, and most academic work, the authors find little evidence to support the view that hedge fund contagion has widespread negative effects on markets and mispricing.
• DeVault, L., & Sias, R. W. (2017). Hedge Fund Politics and Portfolios. Journal of Banking and Finance.
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  Consistent with the well-documented relation between political orientation and psychological traits, hedge funds’ political orientations are related to their portfolio decisions. Relative to politically conservative hedge funds, politically liberal hedge funds exhibit a preference for smaller stocks, less mature companies, volatile stocks, unprofitable companies, non-dividend paying companies, and lottery-type securities. Politically liberal hedge funds are also more likely to enter new positions or fully exit existing positions, and make larger adjustments to their U.S. equity market exposure. Our results suggest that psychological characteristics can influence the portfolio decisions of even those at the very top of the financial sophistication ladder.
• Sias, R. W., Turtle, H., & Zykaj, B. (2017). Hedge Fund Return Dependence: Model Misspecification or Liquidity Spirals?. Journal of Financial and Quantitative Analysis, 2157-2181. doi:https://doi.org/10.1017/S0022109017000679
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  We test whether model misspecification or liquidity spirals primarily explain the observed excess dependence in filtered (for economic fundamentals) hedge fund index returns and the links between volatility, liquidity shocks, and hedge fund return clustering. Evidence supports the model misspecification hypothesis: (i) hedge fund filtered return clustering is symmetric, (ii) filtered short bias fund returns exhibit negative dependence with filtered returns for other hedge fund types, (iii) negative liquidity shocks are associated with clustering in both tails and market volatility subsumes the role of negative liquidity shocks, and (iv) these same patterns appear in size-sorted equity portfolios.
• Sias, R. W., Turtle, H. J., & Zykaj, B. (2016). Hedge Fund Crowds and Mispricing. Management Science.
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  Recent models and the popular press suggest that large groups of hedge funds follow similar strategies resulting in crowded equity positions that destabilize markets. Inconsistent with this assertion, we find that hedge fund equity portfolios are remarkably independent. Moreover, when hedge funds do buy and sell the same stocks, their demand shocks are, on average, positively related to subsequent raw and risk-adjusted returns. Even in periods of extreme market stress, we find no evidence that hedge fund demand shocks are inversely related to subsequent returns. Our results have important implications for the ongoing debate regarding hedge fund regulation. (Key words: hedge funds, crowds, market efficiency)
• Sias, R. W., & Devault, L. A. (2014). Hedge Fund Politics and Portfolios. SSRN Electronic Journal. doi:10.2139/ssrn.2539807
• Choi, N., & Sias, R. W. (2012). Why Does Financial Strength Forecast Stock Returns? Evidence for Subsequent Demand by Institutional Investors. Journal of Financial Economics, 25(5), 1550-1587.
• Knewtson, H. S., & Sias, R. W. (2010). Why Susie owns Starbucks: The name letter effect in security selection. Journal of Business Research, 63(12), 1324-1327. doi:10.1016/j.jbusres.2009.12.003
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  Abstract We examine whether security selection is influenced by the name letter effect—a psychological predisposition to select items that start with leading own name letters. Two sets of tests reveal evidence that the name letter effect influences investors' security selection decisions. First, breadth of ownership (as measured by the number of institutional investors holding the security) is positively related to U.S. name letter frequency, e.g., stocks that begin with the common name letter “M” exhibit a greater number of institutional shareholders than stocks that begin with the less common name letter “X.” Second, undergraduate students managing university endowment funds are more likely to select securities for evaluation when the stock's name begins with the same letter as their name.
• Knewtson, H. S., Sias, R. W., & Whidbee, D. A. (2010). Style Timing with Insiders. Financial Analysts Journal, 66(4), 46-66. doi:10.2469/faj.v66.n4.7
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  Aggregate demand by insiders predicts time-series variation in the value premium. Insider trading forecasts the value premium because insiders sell (buy) when markets—especially growth stocks—are overvalued (undervalued). This article suggests that investors can use signals from aggregate insider behavior to adjust style tilts and exploit sentiment-induced mispricing.
• Sias, R. W., & Whidbee, D. A. (2010). Insider Trades and Demand by Institutional and Individual Investors. Review of Financial Studies, 23(4), 1544-1595. doi:10.1093/rfs/hhp114
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  There is a strong inverse relation between insider trading and institutional demand the same quarter and over the previous year. Our analysis suggests a combination of factors contribute to this relation. First, institutional investors are more likely to provide the liquidity necessary for insiders to trade. Second, insiders are more likely to buy low valuation and low lag return stocks while institutions are attracted to the opposite security characteristics. Last, the results are consistent with the hypothesis that insiders are more likely to view their securities as overvalued (undervalued) following a period when institutions were net buyers (sellers). The Author 2010. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.
• Sias, R. W., & Choi, N. Y. (2009). Institutional industry herding.. Journal of Financial Economics, 94(3), 469-491. doi:10.1016/j.jfineco.2008.12.009
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  Abstract We examine whether institutional investors follow each other into and out of the same industries. Our empirical results reveal strong evidence of institutional industry herding. The cross-sectional correlation between the fraction of institutional traders buying an industry this quarter and the fraction buying last quarter, for example, averages 40%. Additional tests suggest that correlated signals primarily drive institutional industry herding. Our results also provide empirical support for “style investing” models.
• Sias, R. W. (2007). Causes and Seasonality of Momentum Profits. Financial Analysts Journal, 63(2), 48-54. doi:10.2469/faj.v63.n2.4521
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  With Januaries (a month in which lagged “losers” typically outperform lagged “winners”) excluded, the average monthly return to a momentum strategy for U.S. stocks was found to be 59 bps for non-quarter-ending months but 310 bps for quarter-ending months. The pattern was stronger for stocks with high levels of institutional trading and was particularly strong in December. The results suggest that window dressing by institutional investors and tax-loss selling contribute to stock return momentum. Investors using a momentum strategy should focus on quarter-ending months and securities with high levels of institutional trading.
• Sias, R. W. (2007). Reconcilable Differences: Momentum Trading by Institutions. The Financial Review, 42(1), 1-22. doi:10.1111/j.1540-6288.2007.00159.x
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  A growing literature evaluates the relation between lag returns and demand by institutional investors. Given that lag returns and institutional ownership are directly observable, it is surprising that previous tests yield dramatically different conclusions. This study examines differences across studies and finds that four factors account for these discrepancies: (1) value-weighting versus equal-weighting across stocks, (2) averaging versus aggregating over managers, (3) disagreement in the signs of measures of institutional demand, and (4) correlation between current capitalization and both lag returns and measures of institutional demand. Controlling for these factors, the results across different methods are remarkably uniform.
• Bennett, J. A., & Sias, R. W. (2006). Why Company-Specific Risk Changes over Time. Financial Analysts Journal, 62(5), 89-100. doi:10.2469/faj.v62.n5.4285
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  Company-specific risk climbed steadily between 1962 and 1999 in the U.S. market but fell sharply between 2000 and 2003. This article explores the hypothesis that three factors are primarily responsible for observed changes in company-specific risk: changes in the market weights of “riskier” industries, changes in the relative role of small-capitalization stocks in the market, and measurement error associated with changes in within-industry concentration. Empirical tests reveal that each factor contributes to changes in company-specific risk over time and that, combined, these three factors largely explain changes in company-specific risk over the past 40 years.
• Titman, S., Starks, L. T., & Sias, R. W. (2006). Changes in institutional ownership and stock returns: Assessment and methodology. The Journal of Business, 79(6), 2869-2910. doi:10.1086/508002
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  Although the relation between quarterly changes in institutional investor ownership and contemporaneous stock returns is well documented, the source of the relation remains unclear because institutional ownership data are unavailable at higher frequencies. In this study, we develop a method to generate estimates of higher frequency covariances when one variable is observed at lower frequencies (e.g., quarterly changes in institutional ownership and monthly stock returns). Our method provides evidence that institutional trading has both temporary and permanent price effects and that the latter is associated with information effects.
• Cooney, J. W., & Sias, R. W. (2004). Informed trading and order type. Journal of Banking and Finance, 28(7), 1711-1743. doi:10.1016/j.jbankfin.2003.05.004
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  Abstract Each trader must choose between a limit order, a market order, or using a floor broker. We hypothesize that informed investors will: (1) concentrate their trading in floor broker orders and (2) sometimes trade patiently. Consistent with our hypotheses, empirical results suggest that most informed trading occurs through orders executed by floor brokers and that informed floor brokers are sometimes patient. Regardless of their patience, however, quote revisions following trade executions are consistent with the hypothesis that markets recognize that floor traders are more likely to be informed than other traders. As a result, informed trading moves equilibrium security values.
• Bennett, J. A., Sias, R. W., & Starks, L. T. (2003). Greener Pastures and the Impact of Dynamic Institutional Preferences. Review of Financial Studies, 16(4), 1203-1238. doi:10.1093/rfs/hhg040
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  Although institutional investors have a preference for large capitalization stocks, over time they have shifted their preferences toward smaller, riskier securities. These changes in aggregate preferences have arisen primarily from changes in the preferences of each class of institution, rather than changes in the importance of different classes. Evidence also suggests that recent growth in institutional investment combined with this shift in preferences helps explain why markets in general, and smaller stocks in particular, have exhibited greater firm-specific risk and liquidity in recent years. Additional analyses suggest that institutional investors moved toward smaller securities because such securities offer "greener pastures." Copyright 2003, Oxford University Press.
• Parrino, R., Sias, R. W., & Starks, L. T. (2003). Voting with their feet: institutional ownership changes around forced CEO turnover. Journal of Financial Economics, 68(1), 3-46. doi:10.1016/s0304-405x(02)00247-7
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  Abstract We investigate whether institutional investors “vote with their feet” when dissatisfied with a firm's management by examining changes in equity ownership around forced CEO turnover. We find that aggregate institutional ownership and the number of institutional investors decline in the year prior to forced CEO turnover. However, selling by institutions is far from universal. Overall, there is an increase in shareholdings of individual investors and a decrease in holdings of institutional investors who are more concerned with holding prudent securities, are better informed, or are engaged in momentum trading. Measures of institutional ownership changes are negatively related to the likelihoods of forced CEO turnover and that an executive from outside the firm is appointed CEO.
• Bennett, J. A., & Sias, R. W. (2001). Can Money Flows Predict Stock Returns. Financial Analysts Journal, 57(6), 64-77. doi:10.2469/faj.v57.n6.2494
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  “Money flow” is defined as the difference between uptick and downtick dollar trading volume. Despite little published research regarding its usefulness, the measure has become an increasingly popular technical indicator. Our analysis demonstrates that money flows are highly correlated with same-period returns. We also found strong evidence of “money flow momentum,” in that lagged money flows can be used to predict future money flows. Most important is our finding that money flows appear to predict cross-sectional variation in future returns. Their predictive ability is sensitive, however, to the method of money flow measurement (e.g., the exclusion or inclusion of block trades) and the forecast horizon.
• Sias, R. W., Starks, L. T., & Tinic, S. M. (2001). IS NOISE TRADER RISK PRICED. Journal of Financial Research, 24(3), 311-329. doi:10.1111/j.1475-6803.2001.tb00772.x
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  We examine the hypothesis that closed-end fund shareholders garner greater returns than holders of the underlying assets as compensation for bearing “noise trader risk.” We demonstrate that the returns on fund shares are more volatile and exhibit greater mean reversion than the returns on the underlying assets, consistent with the hypothesis that noise traders play a more active role in closed-end fund shares than do the underlying assets. Inconsistent with the De Long et al. (1990) noise trader model, however, we find that after accounting for fund expenses, fund shareholders do not earn returns greater than holders of the underlying assets. JEL classification: G12
• Nofsinger, J. R., & Sias, R. W. (1999). Herding and Feedback Trading by Institutional and Individual Investors. Journal of Finance, 54(6), 2263-2295. doi:10.1111/0022-1082.00188
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  We document strong positive correlation between changes in institutional ownership and returns measured over the same period. The result suggests that either institutional investors positive-feedback trade more than individual investors or institutional herding impacts prices more than herding by individual investors. We find evidence that both factors play a role in explaining the relation. We find no evidence, however, of return mean-reversion in the year following large changes in institutional ownership—stocks institutional investors purchase subsequently outperform those they sell. Moreover, institutional herding is positively correlated with lag returns and appears to be related to stock return momentum. HERDING AND FEEDBACK TRADING HAVE THE POTENTIAL to explain a number of financial phenomena, such as excess volatility, momentum, and reversals in stock prices. Herding is a group of investors trading in the same direction over a period of time; feedback trading involves correlation between herding and lag returns. 1 Although a recent growing body of literature is devoted to investor herding and feedback trading, extant studies take divergent paths. One path depicts individual investors as engaging in herding as a result of irrational, but systematic, responses to fads or sentiment. A second path depicts institutional investors engaging in herding as a result of agency problems, security characteristics, fads, or the manner in which information is impounded in the market.
• Beard, C. G., & Sias, R. W. (1997). Is There a Neglected-Firm Effect?. Financial Analysts Journal, 53(5), 19-23. doi:10.2469/faj.v53.n5.2113
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  The “neglected-firm effect” suggests that securities that analysts ignore offer higher returns (a “neglect premium”) than securities that analysts follow and scrutinize heavily. Using a large and recent sample of securities, we reinvestigated the neglected-firm effect. Controlling for capitalization, we found no evidence of a neglect premium. Investors attempting to exploit the neglected-firm effect during the past 14 years are likely to have been disappointed.
• Sias, R. W. (1997). Optimum Trading Strategies for Closed-End Funds. The Journal of Investing, 6(1), 54-61. doi:10.3905/joi.6.1.54
• Sias, R. W. (1997). PRICE PRESSURE AND THE ROLE OF INSTITUTIONAL INVESTORS IN CLOSED‐END FUNDS. Journal of Financial Research, 20(2), 211-229. doi:10.1111/j.1475-6803.1997.tb00245.x
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  A trader-identified transactions database is employed to investigate: (1) the relation between order-flow imbalance and closed-end fund share prices and discounts; and (2) the role of institutional investors in closed-end funds. Empirical results are consistent with the hypothesis that buyers (sellers) of closed-end funds face upward-downward-) sloping supply (demand) curves. The results also demonstrate that ownership statistics do not accurately reflect institutional investors' importance in the closed-end fund market. The results fail to provide evidence that institutional investors offset the positions of individual investors or that institutional investors face systematic “noise trader risk.”
• Sias, R. W., & Starks, L. T. (1997). Institutions and Individuals at the Turn-of-the-Year. Journal of Finance, 52(4), 1543-1562. doi:10.1111/j.1540-6261.1997.tb01120.x
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  This article evaluates the tax-loss-selling hypothesis against the window-dressing hypothesis as explanations for turn-of-the-year anomalies. We examine differences between securities dominated by individual investors versus those dominated by institutional investors and find that the effect is more pervasive in the former. Controlling for capitalization, we find that in early January (late December), stocks with greater individual investor interest outperform (underperform) stocks with greater institutional investor interest. These results hold for both stocks that previously appreciated in value and stocks that previously depreciated in value. The results are most consistent with the tax-loss-selling hypothesis as an explanation for the turn-of-the-year effect.
• Sias, R. W., & Starks, L. T. (1997). Return autocorrelation and institutional investors. Journal of Financial Economics, 46(1), 103-131. doi:10.1016/s0304-405x(97)00026-3
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  Abstract We propose and test the hypothesis that trading by institutional investors contributes to serial correlation in daily returns. Our results demonstrate that NYSE particles and individual security daily return autocorrelationsare an increasing function of the level of institutional ownership. Moreover, the results are consistent with the hypothesis that institutional trading reflects information and increases the speed of price adjustment. The relation between autocorrelation and institutional holdings does not, however, apparent to be driven by market frictions or rational time-varying required rates of return. We conclude that institutional investors correlated trading patterns contribute to axial correlation in daily returns.
• Sias, R. W. (1996). Volatility and the institutional investor. Financial Analysts Journal, 52(2), 13-20. doi:10.2469/faj.v52.n2.1976
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  Inconsistent with the relationship predicted by most academic theory, a positive contemporaneous association is documented between the level of institutional ownership and security return volatility after accounting for capitalization. This relationship is consistent with two stories: Either riskier securities attract institutional investors, or an increase in institutional holdings results in an increase in volatility. These empirical results are consistent with the latter interpretation.
• Sias, R. W., & Starks, L. T. (1995). The Day-of-the-Week Anomaly: The Role of Institutional Investors. Financial Analysts Journal, 51(3), 58-67. doi:10.2469/faj.v51.n3.1906
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  Studies have suggested that individual investor behavior is the primary cause of the weekend effect. This examination of differences in the daily returns of securities held primarily by individual investors versus securities held by institutional investors indicates that institutional behavior is the primary source of day-of-the-week return differences. Day-of-the-week patterns in returns and volumes are more pronounced in securities in which institutional investors play a greater role.

Presentations

• Sias, R. W. (2020, November). Long-Term Expectations. Georgia State University - invited seminar (virtual due to COVID). Virtual (Georgia State University): Georgia State University.
• Sias, R. W. (2020, November). Long-Term Expectations. University of Leeds invited seminar (virtual due to COVID). Virtual (Leeds University, UK): Leeds University.
• Sias, R. W., Starks, L., & Turtle, H. (2019, Dec). Molecular Genetics, Risk Aversion, Return Perceptions, and Stock Market Participation. Miami Behavioral Finance Conference. Miami University: Miami University.
• Sias, R. W., Starks, L., & Turtle, H. (2019, Spring). Molecular Genetics, Risk Aversion, Return Perceptions, and Stock Market Participation. Rodney L. White Conference on Financial Decisions and Asset Markets. The Wharton School, University of Pennsylvania: Rodney L. White Conference.
• DeVault, L., Sias, R. W., & Starks, L. (2017, April). Sentiment Metrics and Investor Demand. Texas Spring Finance Conference at UT Dallas. UT Dallas: UT Dallas.
• DeVault, L., Sias, R. W., & Starks, L. (2017, September). Sentiment Metrics and Investor Demand. Eller Celebrate Research Presentation. Eller College of Management, University of Arizona: Eller College.
• Sias, R. W. (2016, March). Sentiment Metrics and Investor Demand. University of California, Riverside.
• Sias, R. W. (2016, May). Sentiment Metrics and Investor Demand. VU Amsterdam (Netherlands). Amsterdam Netherlands.
• Sias, R. W. (2016, November 2016). Sentiment Metrics and Investor Demand. University of Massachusetts Amhersts. Amherst, Massachusetts.
• Sias, R. W. (2016, September 2016). Sentiment Metrics and Investor Demand. Colorado State University. Fort Collins, Colorado.
• Sias, R. W. (2015, August). Hedge fund politics and portfolios (coauthor DeVault presented). European Finance Association Meetings. Vienna, Austria.
• Sias, R. W. (2015, December). Who are the sentiment traders? Evidence from the cross-section of stock returns and demand. UC Davis Symposium on Financial Institutions and Intermediaries. UC Davis.
• Sias, R. W. (2015, January). Who are the sentiment traders? Evidence from the cross-section of stock returns and demand (coauthor DeVault presented). American Finance Association. Boston, MA.
• Sias, R. W. (2015, October). Who are the sentiment traders? Evidence from the cross-section of stock returns and demand. University of Waterloo, Waterloo, Canada.
• Sias, R. W. (2015, October). Who are the sentiment traders? Evidence from the cross-section of stock returns and demand. Vienna University of Economics and Business. Vienna Austria.
• DeVault, L., Sias, R. W., & Starks (presenting author), L. (2014, May). Who are the Sentiment Traders? Evidence from the Cross-section of Stock Returns and Demand (coauthor presented). Asian Bureau of Finance and Economic Research (ABFER). Singapore: Asian Bureau of Finance and Economic Research.
• Reca, B., Sias, R. W., & Turtle (presenting author), H. J. (2014, August). Hedge Fund Crowds and Mispricing (coauthor presented). European Finance Association Meetings. Lugano Switzerland: European Finance Associatino.
• Reca, B., Sias, R. W., & Turtle, H. J. (2014, June). Hedge Fund Return Dependence and Contagion. Western Finance Association Meetings. Monterey, CA: Western Finance Association.
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  Different types of hedge funds tend to suffer poor abnormal returns simultaneously. Moreover, the likelihood of clustering in hedge fund left tail abnormal returns is positively related to negative liquidity shocks. These patterns have been interpreted as evidence that hedge funds suffer from liquidity shock induced contagion. We provide novel tests that demonstrate these patterns result from model misspecification and time-varying heteroskedasticity rather than liquidity shock induced contagion. Our results have important implications for understanding how markets function, the role of hedge funds in markets, and hedge fund regulation.
• Reca, B., Sias, R. W., & Turtle, H. J. (2014, March). Hedge Fund Crowds and Mispricing. Finance Down Under Conference. Melbourne Australia: University of Melbourne.
• Sias, R. W. (2013, April). Hedge Fund Crowds and Mispricing. Asian Financial Management Association. Shanghai, China.
• Sias, R. W. (2013, August). Who are the sentiment traders? Evidence from the cross-section of stock returns (coauthor presented). European Finance Association. Cambridge, UK.
• Sias, R. W. (2013, June). Hedge Fund Crowds and Mispricing (coauthor presented). Western Finance Association. South Lake Tahoe, CA.
• Sias, R. W. (2013, October). Hedge Fund Crowds and Mispricing. Cornell University. Cornell, NY.
• Sias, R. W. (2012, September). Hedge Fund Herding: The Apologist Evidence. Texas Tech University. Lubbock, TX.